<![CDATA[Abstract: Digital transformation has encouraged retail companies to leverage information technology and data analytics to improve operational efficiency and the quality of decision-making. The utilization of technology and data analytics enables companies to automate business processes, enhance analytical accuracy, and manage customer data more effectively. This study aims to implement the Naive Bayes algorithm to determine customer discount categories based on purchasing behavior in retail stores as a form of data-driven decision-making that supports digital transformation. The dataset used in this research is the Marketing Campaign Dataset, which consists of the attributes Income, Recency, NumDealsPurchases, NumWebPurchases, NumStorePurchases, and Response. The research stages include data collection, preprocessing, data transformation, classification model development using the Naive Bayes algorithm, and model evaluation using the Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The results show that the Naive Bayes model achieved an accuracy of 70% in classifying customers into low, medium, and high discount categories based on their purchasing behavior patterns. The implementation of this model enables retail companies to provide discount category recommendations more quickly, accurately, and consistently. In addition to improving the effectiveness of promotional strategies, this study demonstrates that the application of machine learning-based data analytics can support digital transformation through faster, more accurate, and data-driven decision-making, thereby helping retail companies improve service quality and business process effectiveness. Keywords: Naive Bayes; Digital Transformation; Customer Discount Categories; Purchasing Behavior; Retail Store. Abstrak: Transformasi digital telah mendorong perusahaan retail untuk memanfaatkan teknologi informasi dan analisis data dalam meningkatkan efisiensi operasional serta kualitas pengambilan keputusan. Pemanfaatan teknologi dan analitik data memungkinkan perusahaan mengotomatisasi proses bisnis, meningkatkan akurasi analisis, serta memanfaatkan data pelanggan secara lebih efektif. Penelitian ini bertujuan mengimplementasikan algoritma Naive Bayes dalam menentukan kategori diskon pelanggan berdasarkan perilaku pembelian pada toko retail sebagai salah satu bentuk pengambilan keputusan berbasis data yang mendukung transformasi digital. Dataset yang digunakan adalah Marketing Campaign Dataset yang terdiri dari atribut Income, Recency, NumDealsPurchases, NumWebPurchases, NumStorePurchases, dan Response. Tahapan penelitian meliputi pengumpulan data, preprocessing, transformasi data, pembentukan model klasifikasi menggunakan algoritma Naive Bayes, serta evaluasi menggunakan Confusion Matrix, Accuracy, Precision, Recall, dan F1-Score. Hasil penelitian menunjukkan bahwa model Naive Bayes memperoleh akurasi sebesar 70% dalam mengklasifikasikan pelanggan ke dalam kategori diskon rendah, sedang, dan tinggi berdasarkan pola perilaku pembelian. Implementasi model ini mampu membantu perusahaan retail dalam memberikan rekomendasi kategori diskon secara lebih cepat, tepat, dan konsisten. Selain meningkatkan efektivitas strategi promosi, penelitian ini menunjukkan bahwa penerapan analitik data berbasis machine learning dapat mendukung transformasi digital melalui pengambilan keputusan yang lebih cepat, akurat, dan berbasis data sehingga membantu perusahaan retail meningkatkan kualitas layanan serta efektivitas proses bisnis. Kata kunci: Naive Bayes; Transformasi Digital; Kategori Diskon Pelanggan; Perilaku Pembelian; Toko Retail.]]>
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